Leveraging Machine Learning for Optimal Trade Entry Point Identification in the Cryptocurrency Market

Subject Areas : Intelligent Multimedia Processing and Communication Systems (IMPCS)

Abulfazl Yavari ¹ ( Assistant Professor, Faculty of Computer Engineering and IT, Payam-e Noor University, Tehran, Iran)
Hasan Aama ² (Assistant Professor, Faculty of Management Economics and Accounting, Payam-e Noor University, Tehran, Iran)
Seyyed Mohammad R. Hashemi ³ (Assistant Professor, Faculty of Computer Engineering, Skill National University, Tehran, Iran)

Received: 2024-04-13 Accepted : 2024-06-01 Published : 2024-06-21

Keywords: Cryptocurrency market, market forecasting, artificial neural network, support vector machine, nearest neighbor, technical analysis,

Abstract :

Introduction: In the domain of financial forecasting, machine learning (ML) models have garnered significant attention in recent years. One prominent application lies in the cryptocurrency market, where intelligent trading bots facilitate a substantial portion of daily transactions.

Method: This paper investigates the efficacy of ML-based methods for identifying optimal trade entry points. Specifically, we employ the Relative Strength Index (RSI) and Simple Moving Average (SMA) indicators to extract a set of trend and crossover features. Subsequently, these features are utilized to train multilayer neural network, support vector machine, and nearest neighbor learning models. The performance of each model is then evaluated using digital currency market data encompassing the first seven months of 2023 for a variety of cryptocurrencies.

Results: Our findings demonstrate that, firstly, the extracted features exhibit promising efficacy. Secondly, the nearest neighbor model achieved the highest profitability during the evaluation period compared to the other investigated models.

Discussion: In the research conducted in this field, technical indicators are often used directly in market forecasting but in the proposed method of this article, instead of directly using the values of the indicators as the input of the classification methods, trend behaviors and their intersections have been used. In the continuation of this research, it is possible to determine the best exit points from the transaction with the help of machine learning and the use of volume indicators in the learning process.

References:

[1] E. Fama. “Efficient capital markets: A review of the theory”. The Journal of Finance, 25, 383–417, 1970
[2] J. Almeida, T. Cruz,”A systematic literature review of investor behavior in the cryptocurrency markets”, Journal of Behavioral and Experimental Finance, Volume 37, 2023
[3] V. Derbentsev, V. Babenko, K. Khrustalev, H. Obruch and S. Khrustalova, “Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices”. International Journal of Engineering, 34, 140–148, 2021
[4] C. Conrad, A. Custovic and E. Ghysels, “Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis”. Journal of Risk and Financial Management, 11(2), 23, 2018
[5] Y. Liu and A. Tsyvinski, “Risks and returns of cryptocurrency”. The Review of Financial Studies, 34(6), 2689–2727, 2021
[6] R. C. Phillips and D. Gorse, “Predicting cryptocurrency price bubbles using social media data and epidemic modelling”. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE, 2017
[7] F. Valencia, A. Gomez-Espinosa and B. Valdes-Aguirre, “Price movement prediction of cryptocurrencies using sentiment analysis and machine learning”. Entropy, 21(6), 589, 2019
[8] A. Yavari, H. Hassanpour, B. Rahimpour and M. Mahdavi, “Event prediction in social network through Twitter messages analysis. Social Network Analysis and Mining”. 12. 10.1007/s13278-022-00911-x. 2022
[9] E. Bouri, C. K. Lau, B. Lucey and D. Roubaud, “Trading volume and the predictability of return and volatility in the cryptocurrency market”. Finance Research Letters, 29, 340–346, 2019
[10] W. Zhengyang, L. Xingzhou, R. Jinjin and K. Jiaqing, “Prediction of cryptocurrency price dynamics with multiple machine learning techniques”. In Proceedings of the 2019 4th International Conference on Machine Learning Technologiesو (pp. 15–19), 2019
[11] Hajighorbani, Hashemi, Broumandnia, faridpour, “a review of some semi-supervised learning methods”, Journal of Knowledge-Based Engineering and Information”, 2(4), 2013
[12] S. Ansaripour, T. J. Gandomani, “Comparison of the classification methods in software development effort estimation”, Intelligent Multimedia Processing and Communication Systems(IMPCS), 3(2), 11-18, 2022
[13] T. E. Koker and D. Koutmos, “Cryptocurrency trading using machine learning”. Journal of Risk and Financial Management, 13(8), 178, 2020
[14] G. Attanasio, L. Cagliero, P. Garza and E. Baralis, “Quantitative cryptocurrency trading: Exploring the use of machine learning techniques”. In In Proceedings of the 5th Workshop on Data Science for Macro-Modeling with Financial and Economic Datasetsو (pp. 1–6). 2019.
[15] E. Akyildirim, A. Goncu and A. Sensoy, “Prediction of cryptocurrency returns using machine learning”. Annals of Operations Research, 297(1), 3–36, 2021
[16] Z. Dongna, H. Xihui, L. Chi Keung and X. Bing, “Implications of cryptocurrency energy usage on climate change”, Technological Forecasting and Social Change”, Volume 187, 2023,
[17] M. Kumbure, C. Lohrmann, P. Luukka and J. Porras, “Machine learning techniques and data for stock market forecasting: A literature review”, Expert Systems with Applications”, Volume 197, 2022